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Medicine Group Review Article 記事ID: igmin231

A Study of Multi-Pose Effects On a Face Recognition System

Forensic Medicine Affiliation

Affiliation

    Shanghai Research Institute of Criminal Science and Technology, Shanghai Key Laboratory of Crime Scene Evidence, Shanghai, PR China

要約

Interpersonal and intrapersonal face variation interference caused by multiple poses is challenging for distance-based face recognition systems. In this paper, we investigate the face-feature distance distribution for Chinese multi-pose faces. The simulation shows that the number of individuals in the gallery database will greatly affect the recognition performance for near-profile face images. It also provides a prediction of the Top-N occurrence rates in different gallery-size environments.

数字

参考文献

    1. Elharrouss O, Almaadeed N, Al-Maadeed S, Khelifi F. Pose-invariant face recognition with multitask cascade networks. Neural Comput Appl. 2022;34(8):6039-6052. doi:10.1007/s00521-022-07668-3.
    2. Guan Y, Fang J, Wu X. Multi-pose face recognition using Cascade Alignment Network and incremental clustering. Signal Image Video Process. 2021;15(1):63-71. doi:10.1007/s11760-020-01832-4.
    3. He H, Liang J, Hou LX, Yunfei L. Multi-pose face reconstruction and Gabor-based dictionary learning for face recognition. Appl Intell. 2023;53(13):16648-16662. doi:10.1007/s10489-023-02954-1.
    4. Tu X, Zhao J, Liu Q, WenjieGuo, GuodongLi, ZhifengLiu, WeiFeng, Jiashi. Joint Face Image Restoration and Frontalization for Recognition. IEEE Trans Circuits Syst Video Technol. 2022;32(3):1285-1298. doi:10.1109/TCSVT.2021.3077655.
    5. Ding C, Xu C, Tao D. Multi-Task Pose-Invariant Face Recognition. IEEE Trans Image Process. 2015;24(3):980-993. doi:10.1109/TIP.2014.2383290.
    6. Luan X, Geng H, Liu L, Li W, Ren M. Geometry Structure Preserving based GAN for Multi-Pose Face Frontalization and Recognition. IEEE Access. 2020;PP(99):1-1. doi:10.1109/ACCESS.2020.2997572.
    7. Zeng K, Wang Z, Han CZ. Implicit space pose consistent transfer network for deep face verification. Pattern Recognit Lett. 2023;176:1-6. doi:10.1016/j.patrec.2022.12.009.
    8. Philbin FSDKJ. FaceNet: A unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA; 2015:815-823. doi:10.1109/CVPR.2015.7298682.
    9. Wen G, et al. The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Trans Syst Man Cybern. 2008;38(1):149-161. doi:10.1109/TSMCB.2007.906173.
    10. Limin B, Mingxing J. A Facial Point Detection and Face Recognition Algorithm Based on Multi-pose (in chi). Comput Digit Eng. 2018;46(7):1440-1445,1457. doi:10.3969/j.issn.1672-9722.2018.07.035.
    11. Phillips PJ, Martin A, Wilson CL, Przybocki M. An introduction evaluating biometric systems. Comput. 2000;33(2):56-63. doi:10.1109/2.826988.
    12. Prince SJD, Elder JH, Jonathan W, Felisberti FM. Tied factor analysis for face recognition across large pose differences. IEEE Trans Pattern Anal Mach Intell. 2008;30(6):970-984. doi:10.1109/TPAMI.2007.70753.

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